Exhaustive Comparison of Coding LLMs 2026: Reviewing GPT, Claude, Gemini, Codestral, DeepSeek, and Llama by Popular Programming Language

When it comes to coding with generative AI, even if you say “code generation,” each LLM has clearly different strengths. The gaps show up most in:
(1) the ability to read and modify large codebases,
(2) the fix loop after tests fail,
(3) handling “real-world development” including types, builds, and dependencies, and
(4) consistency across multiple languages and files.
If you misread these differences, you can end up with output that looks clean but doesn’t run—and a lot more review work than expected.

Quick Takeaways (a map before you read)

• If you want to drive large refactors or bug fixes at the repository level, models that perform well on SWE-bench-style tasks should be your anchor (e.g., GPT-4.1, Claude 3.5 Sonnet, Gemini 3 Pro, etc.).
• For single-function generation or autocomplete, code-specialized models (Codestral, DeepSeek Coder, Code Llama family) can absolutely compete—but gaps often appear in multi-file coordination and architectural judgment.
• Popular languages (JavaScript/TypeScript, Python, SQL, Java, C#, C++, etc.) generally have higher success rates thanks to abundant training data and examples. As a proxy for “popularity,” this article assumes Stack Overflow usage data.
• For areas with many “environment/type/behavior traps” like Rust/C++/Shell, every model tends to require an operation model built around testing and execution. In these domains, the validation loop often matters more than raw model “smartness.”

Who This Article Helps (concretely)

This comparison is especially useful for:

First, developers who use JavaScript/TypeScript, Python, or SQL daily and repeatedly do similar spec changes and bug fixes. The right LLM choice can dramatically change perceived speed and review load.

Second, teams working in strongly typed languages like Java/C#, where API boundaries, exception design, and async behavior heavily shape quality. Here, getting pulled into “plausible but wrong” answers can cause real incidents—so knowing strengths and proper separation of roles matters.

Third, environments like C++/Rust/Go where build systems, dependencies, memory safety, and concurrency constraints are strict. LLMs can write code that doesn’t compile or doesn’t pass tests, so it’s safer to anchor on models that excel at test-generation and fix loops.

Finally, people working on large products where consistency (naming, architecture, exception policy) and governance (reviews, audit trails) are required. Benchmark signals and design differences under “agent-driven execution” become more important.

LLMs Covered (model families commonly chosen in early 2026 practice)

This article covers the following “families” commonly adopted in real-world work:

• OpenAI: GPT-4.1, GPT-5.2 family (with coding indicators such as SWE-bench publicly discussed)
• Anthropic: Claude 3.5 Sonnet (with SWE-bench Verified results and agent-setup discussion)
• Google: Gemini 3 Pro (official pages provide agent-style evaluations and metrics such as SWE-bench Verified)
• Mistral: Codestral 25.01 (code-specialized; often referenced in contexts like HumanEval)
• DeepSeek: DeepSeek-Coder-V2 (paper summarizes coding benchmark results)
• Meta: Code Llama (officially describes evaluation approaches using HumanEval, etc.)

Benchmarks are not universal. They can be biased toward certain task shapes (single-function, patch generation, agent execution). For example, SWE-bench Verified uses real repositories, but results can depend on evaluation conditions and scaffolding. Referencing the official leaderboard is assumed.
For multilingual evaluation, there are also efforts like MultiPL-E and Aider Polyglot that measure performance across multiple languages.

The Big Picture First: “What Kind of Work Each Model Is Good At,” Based on Benchmarks

Rather than ranking models in a single line, it’s easier to choose by first classifying what kinds of work they excel at.

1) Repo-level fixes / bug fixing (closest to real development)

The highest business value often comes from the full loop: patch an existing codebase and get tests passing. SWE-bench Verified is often referenced here, and public results exist for models such as GPT-4.1 and Claude 3.5 Sonnet.
Gemini 3 Pro also publishes agent-style evaluation and SWE-bench Verified metrics on official pages.
Official leaderboards allow multi-model comparisons (watch the conditions; still useful for relative intuition).

2) Small implementations / autocomplete / single-function generation (speed-focused)

Benchmarks like HumanEval resemble “write a function and pass tests,” where code-specialized models often shine. Codestral 25.01 is sometimes discussed via HumanEval scores.
However, this task shape often excludes multi-file coherence and architectural judgment, so later-stage product work may depend more on editability, fix loops, and validation.

3) Multi-language / multi-environment (JS/TS, Java, C++, Rust, etc.)

Real teams don’t run only Python. Aider Polyglot is designed to measure cross-language editing and fixing across C++/Go/Java/JavaScript/Python/Rust.
MultiPL-E translates HumanEval/MBPP into multiple languages to help surface language-specific differences.

Why We Use “Popular Languages” as the Baseline

When discussing model strengths/weaknesses, language popularity matters. In simple terms, the more samples, public code, and discussions a language has, the easier it is for LLMs to learn typical patterns.
In Stack Overflow’s 2025 survey, languages like JavaScript, HTML/CSS, SQL, Python, TypeScript, Java, C#, and C++ rank near the top. These are treated as “popular languages” for the review axes below.

Personality by LLM Family (high-level, operational view)

Rather than micro-ranking, this section summarizes how each family tends to behave in practice.

GPT family (OpenAI)

• Strength: stable adherence to instructions and editing existing code; public SWE-bench Verified results for GPT-4.1 are often cited.
• The GPT-5.2 family is also referenced in relation to SWE-bench Verified (verify measurement and conditions, but it helps with directional intuition).
  Operational tip: fix the requirements, scope, and acceptance criteria (tests/compat/perf) in short form, then run a tight iteration loop.

Claude family (Anthropic)

• Claude 3.5 Sonnet has published SWE-bench Verified results and guidance on agent setups to unlock better performance.
  In many teams, it pairs well with specs and review writing—useful when you want to proceed while documenting intent.

Gemini family (Google)

• Gemini 3 Pro publishes agent-style coding evaluation metrics (including SWE-bench Verified) on official pages.
  It often shines when asked to organize complex tasks (code + surrounding context + procedures), especially when paired with tool usage and verification flows.

Codestral (Mistral)

• Codestral 25.01 is positioned as code-specialized; HumanEval is often mentioned in discussions of it.
  It tends to be strong for function generation, autocomplete, and refactor “prep,” while multi-file coordination can be a common weakness (treat external reviews as hints and validate on your own repo).

DeepSeek Coder

• DeepSeek-Coder-V2 summarizes benchmark results in a paper and is frequently referenced as a strong open-model option.
  It can be attractive when you have “on-prem/local,” cost-optimization, or strict data-exfiltration constraints.

Code Llama (Meta)

• Code Llama describes evaluation approaches using HumanEval, etc., in official materials.
  Teams that can build open-model operations (context management, tool connectors, evaluation) can use it very effectively in scoped use cases.

By Popular Language: Practical Strengths/Weaknesses (where real differences show)

Now we get to the core. Rather than overconfident ranking, this section summarizes real-world tendencies about “who wins more often” per language.
The selection of popular languages follows top usage in Stack Overflow’s survey.

1) JavaScript / TypeScript

Why it tends to go well: massive example volume and abundant real-world patterns across both front and back ends.
Model fit

• GPT: often fast at assembling “common implementations” (React/Next) and relatively stable at applying diffs to existing code; repo-fixing strength helps.
• Claude: good when you want to proceed while organizing UI specs, state transitions, and accessibility considerations in text.
• Gemini: tends to work well when you ask for steps plus verification (tests/build/type checks) as a package.
  Common pitfalls
• Advanced TypeScript (conditional types, generics, schema inference like Zod) can compile yet be semantically off.
• In frontend work, “it runs” ≠ “it meets UX requirements,” so pair it with screen checks and E2E tests.

Mini request template

• Goal: prevent double submission on a form
• Scope: only src/components/CheckoutForm.tsx and useCheckout.ts
• Acceptance: no type errors; disable button while submitting; transition only on success; update existing tests

2) Python

Why it tends to go well: widely used for AI/data/automation, with abundant training examples and task patterns. Python usage is high on Stack Overflow, and adoption growth is also reflected there.
Model fit

• Codestral/DeepSeek: often strong for single-function generation and scripts (close to the HumanEval task shape).
• GPT/Claude/Gemini: for “product Python” (FastAPI/Django, dependency management, tests, typing), fix-loop strength matters; SWE-bench-style signals are useful.
  Common pitfalls
• Dependency and environment issues (pip/poetry, OS/CUDA/version mismatches) can’t be solved by code alone. LLMs can propose “how to run,” but you still need execution and logs.

Mini instruction to reduce failures

• “Python 3.11, poetry, pytest, ruff, mypy. Do not change existing function signatures. Add tests first, then fix.”

3) SQL

Why it tends to go well: patterns are explicit, and the loop of explain → generate → refine is straightforward. SQL also ranks high in usage.
Model fit

• Claude/GPT: often good at converting text requirements into JOIN/aggregation design while explaining the meaning.
• Gemini: can work well when paired with procedures (EXPLAIN checks, index suggestions, test data design).
  Common pitfalls
• Dialects (PostgreSQL/MySQL/BigQuery/SQL Server) break queries. Always specify the DB and iterate using EXPLAIN output.
• “Correctness” depends on schema and data assumptions—without sample schema, LLMs can be elegantly wrong.

Mini request with schema

• Table definitions (PK/FK, scale/row counts)
• Expected output example (columns, grain)
• Constraints (runtime limit, whether index changes are allowed)

4) Java

Why it tends to go well: deep enterprise accumulation and strong patterns (DI, DTO, exception design). Also high usage.
Model fit

• GPT/Gemini: often good at repo edits and test-fix iteration; patching aptitude helps indirectly.
• Claude: pairs well with “lock the intent in writing first” (where to catch exceptions, boundary responsibilities) and then implement.
  Common pitfalls
• Framework dependency (Spring Boot, Gradle/Maven, annotations, profiles) can cause “code is right but it won’t start.”
• Generics/Streams: if the model optimizes for shortest code, readability can degrade—providing style rules helps.

5) C# (.NET)

Why it tends to go well: many examples in enterprise and game dev (Unity); APIs and patterns are fairly standardized. High popularity too.
Model fit

• GPT/Claude: often practical for team development when you want review notes and design explanations alongside code.
• Open models (Codestral/DeepSeek/Llama): helpful in scoped tasks, but can break when project templates and NuGet dependencies are involved—environment drift matters.
  Common pitfalls
• Async exception propagation, CancellationToken usage, DI lifecycle coherence can produce “works but dangerous” code.
• Fix short policies in the request (“exception policy,” “logging policy,” “nullable,” “no sync blocking”) to stabilize.

6) C / C++

Why it’s tricky: very popular, but loaded with factors LLMs can’t guarantee via text alone—build systems, undefined behavior, memory management, ABI constraints. Popularity is still high in rankings.
Model fit

• GPT/Claude/Gemini: best used in a fix loop with tests plus ASan/UBSan/static-analysis logs.
• Code-specialized models: useful for function-level transforms or small optimization prep, but multi-dependency build/ABI work is where they fail more often.
  Common pitfalls
• UB (sign/overflow/bounds/lifetimes) and ownership issues lead to “plausible” but unsafe output.
• Provide repro steps, crash logs, and compiler flags; lock acceptance to tests and sanitizers.

Mini acceptance criteria

• Zero warnings with -Wall -Wextra -Werror
• Zero sanitizer findings (ASan/UBSan)
• Do not change public API signatures

7) Go

Why it tends to go well: large standard library surface, strong formatting and conventions, so outputs tend to be clean. It’s also in the upper popularity group.
Model fit

• GPT/Gemini: solid when you iterate with tests around concurrency and HTTP.
• Claude: safer when you solidify design intent (context propagation, responsibility splits) in writing first.
  Common pitfalls
• Goroutine leaks, missing context cancellation, and race conditions are easy to miss by reading alone. Use the race detector and load-test logs to drive iterations.

8) Rust

Why it’s a “hard zone”: popularity is growing, but ownership/lifetimes and trait bounds often require iterations before type-correct convergence. Among popular languages, it’s closer to the “hard mode” end.
Model fit

• GPT/Claude/Gemini: success improves when you paste compiler errors and ask for “minimal changes” repeatedly.
• Code-specialized models: can write short algorithms, but often break at crate structure and error-handling policies—keep scope small.
  Common pitfalls
• To silence lifetime errors, suggestions may distort design intent. Explicit constraints help (“ownership model,” “keep public API,” “no Clone,” etc.).

9) Bash / Shell

Why it’s deceptively hard: usage is high (also near the top in surveys), but there are many “mines”: environment differences, quoting, paths, permissions. Writing truly safe shell scripts is harder than it looks.
Model fit

• Claude/GPT: quality improves when you request safety measures (set -euo pipefail, input validation, dry-run) with explanations.
• Gemini: can feel safer when paired with procedures (run examples, expected output, rollback steps).
  Common pitfalls
• Destructive commands (rm, chmod, sed -i) and OS differences (GNU vs BSD) are common accident sources. Standardizing a team “safe template” is recommended.

How to Read Benchmarks (common misunderstandings)

Here are the most common pitfalls in LLM comparisons:

• SWE-bench Verified is closer to real work (real repo fixes), but scores can vary with agent scaffolding and conditions. Use the official leaderboard and compare under the same conditions.
• Single-function tasks like HumanEval measure “can you hit the function,” but don’t capture project structures and design constraints well.
• Multi-language frameworks like MultiPL-E help you see language differences.
• Benchmarks like Aider Polyglot include multi-language editing plus test feedback, pushing closer to “real fixing power.”

Practical “Model Use Patterns” That Reduce Failure (usable as-is)

Often, the “how you use it” matters more than which model you choose. These patterns raise win rates across almost any LLM:

1. Fix the request to only three items: goal, scope, acceptance criteria
2. Ask for tests (or reproduction steps) first, then ask for the fix
3. Don’t ask for everything at once—split tasks so diffs stay small
4. Pre-declare language-specific traps (TS types, Rust ownership, C++ UB, Shell env drift) as explicit constraints

Mini common template

• Goal: satisfy X (e.g., eliminate N+1 queries)
• Scope: list of files allowed to change
• Acceptance: tests, types, lint, compatibility, performance conditions

Conclusion: Choose LLMs by “What Kind of Mines Exist” in Each Language

Popular languages (JS/TS, Python, SQL, Java, C#, etc.) tend to succeed across most LLMs, but mines are obvious: advanced TS types, Java/C# framework dependency, SQL dialect drift, and so on.
For C++/Rust/Shell, the deciding factor is often not “writing ability,” but whether your operation model can converge via logs and tests.
And in repo-level real development, selecting with “fixing power” in mind—via signals like SWE-bench families—often improves real productivity.

References

• Stack Overflow Developer Survey 2025 – Technology
• OpenAI – Introducing GPT-4.1 in the API (mentions SWE-bench Verified)
• Anthropic – Claude 3.5 Sonnet SWE-bench (SWE-bench Verified + agent explanation)
• Google DeepMind – Gemini 3 Pro (metrics incl. SWE-bench Verified)
• SWE-bench – Official Leaderboards
• Mistral – Codestral 25.01
• DeepSeek – DeepSeek-Coder-V2 paper (arXiv)
• Meta – Introducing Code Llama
• Aider – LLM Leaderboards (Polyglot explanation)
• Epoch AI – Aider Polyglot (benchmark explanation)
• MultiPL-E – GitHub